Thanks for the answer. It is helpful. What I would like to do is make a Gaussian normal curve that will superimpose itself over bar graphs showing a population distribution. The idea is to give an immediate visual impression of how far from "normal" the population data is given the population mean and std dev. I haven't had success with this. I can't seen to get it right. What I see is a much higher peak of my normal curve than what I see in my data. I have attached a plot in fact. Here is the plot file for this. (I use a big bash script to see this stuff up for gnuplot.)
set key left Left u=13.35000000000000000000 var=.47548245614035087719 display_v=.475 display_u=13.350 set label 1 "mean = 13.350 " at 15.2,.13205 right set label 2 "std dev = .689 " at 15.2,.13205*.95 right set label 3 "std error mean = .052 " at 15.2 ,.13205*.90 right set label 4 "count = 172 " at 15.2,.13205*.85 right std=.68955235924500387223 count=172 stderrormean=.0027 set ylabel "Result Result Frequency" set xlabel " " f(x)=exp(-((x-u)**2/(2*var)))/(sqrt(2*pi*var)) plot "/tmp/plot_data_bar" using 2:1 notitle with boxes , f(x) Note: My graph labels get squashed when I add the Normal curve. That's on my todo list after I understand what these normal curves really mean. BTW, gnuplot is lots of fun and it seems to work for my simple needs. Joel
gauss.ps
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